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A N
etw
orke
d A
ppro
ach
to S
pect
rum
Se
nsin
g in
Cog
nitiv
e R
adio
Sys
tem
s
Cla
udio
da
Silv
a
Join
t wor
k w
ith C
hris
Hea
dley
, Jes
se R
eed,
Bria
n C
hoi,
and
Am
y M
alad
y.
Invi
ted
pres
enta
tion
at:
Cla
udio
da
Silv
a
Intro
duct
ion
•The
app
licat
ion
of c
ogni
tion
to c
omm
unic
atio
ns ra
dios
and
net
wor
ks
open
s ne
w a
nd e
xciti
ng o
ppor
tuni
ties
for i
mpr
ovin
g th
e tra
nsm
issi
on o
f in
form
atio
n
•Firs
t app
licat
ion
(?):
Opp
ortu
nist
ic s
pect
rum
re-u
se –
e.g.
, 802
.22
•In
an o
ppor
tuni
stic
spe
ctru
m a
cces
s sc
enar
io, h
arm
ful i
nter
fere
nce
and
perfo
rman
ce d
egra
datio
n m
ay re
sult
if sp
ectru
m s
ensi
ng is
not
relia
ble
•Fac
t: Sk
eptic
ism
•His
tory
: Ove
rlay
anal
og c
ellu
lar n
etw
ork,
UW
B (D
AA
–e.
g., E
urop
e)•M
ore
rese
arch
is n
eede
d
Cla
udio
da
Silv
a
Spe
ctru
m S
ensi
ng
•Doe
s it
fall
into
the
“bea
ten
to d
eath
” cat
egor
y?
•Com
mer
cial
sys
tem
s →
firs
t tim
e M
ilita
ry s
yste
ms →
hard
to k
now
, diff
eren
t req
uire
men
ts a
nd a
pplic
atio
ns
•Opp
ortu
nist
ic s
pect
rum
acc
ess
requ
ires
guar
ante
es
•Do
thes
e gu
aran
tees
lead
to re
ason
able
requ
irem
ents
?
Prob
lem
: The
oret
ical
bou
nds
on th
e sp
ectru
m s
ensi
ng p
erfo
rman
ce
Cla
udio
da
Silv
a
Spe
ctru
m S
ensi
ng: U
nkno
wn
Cha
nnel
•C
ompl
ex ta
sk:
•No
know
ledg
e of
the
trans
mitt
ed d
ata
and
man
y un
know
n pa
ram
eter
s (s
igna
l pow
er, c
arrie
r fre
quen
cy a
nd p
hase
offs
ets,
tim
ing
info
)•N
o ch
anne
l kno
wle
dge
•Exa
mpl
e: T
he d
evel
opm
ent o
f sig
nal d
etec
tion
and
clas
sific
atio
nal
gorit
hms
for m
ultip
ath
fadi
ng c
hann
els
is s
urpr
isin
gly
scar
ce
-20
-15
-10
-50
510
1520
0.5
0.550.6
0.650.7
0.750.8
0.850.9
0.951
SN
R (
dB)
Average Probability of Correct Classification
BP
SK
/QP
SK
Cla
ssifi
er (
1000
0 Tr
ials
, N
clas
s = 1
0)
P
erfe
ct E
stim
ates
MoM
Est
imat
es o
f α,
θM
oM E
stim
ates
of α
, θ,
ε (
Nes
t = 1
0)
MoM
Est
imat
es o
f α, θ
, ε
(Nes
t = 1
00)
-25
-20
-15
-10
-50
510
1520
250.
1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.91
SN
R (
dB)
Average Probability of Correct ClassificationBP
SK
/QP
SK
/8-P
SK
/16-
PS
K/1
6-Q
AM
/64-
QA
M C
lass
ifier
(10
00 T
rials
, N
clas
s = 1
00)
P
erfe
ct E
stim
ates
MoM
Est
imat
es o
f α,
θM
oM E
stim
ates
of α
, θ,
ε (
Nes
t = 1
00)
Prob
lem
: Per
form
ance
lim
its o
f sen
sing
alg
orith
ms
for r
ealis
tic
impl
emen
tatio
ns a
nd p
ract
ical
sce
nario
s?
Cla
udio
da
Silv
a
Dis
tribu
ted
Sen
sing
Why
? It
onl
y m
akes
sen
se•R
educ
e de
tect
ion
requ
irem
ents
of i
ndiv
idua
l sen
sors
•T
ake
adva
ntag
e of
the
radi
o si
gnal
var
iabi
lity
•Spe
ctru
m u
tiliz
atio
n is
a s
patia
l phe
nom
enon•U
ser 2
mig
ht n
ot d
etec
t the
pr
imar
y us
er
•Tog
ethe
r, us
ers
1, 2
, and
3 h
ave
a hi
gher
pro
babi
lity
of d
etec
ting
and
clas
sify
ing
othe
r sys
tem
s
→di
vers
ity
→hi
dden
nod
es
•Nod
e-pr
oces
sing
→
Det
ectio
n/cl
assi
ficat
ion
algo
rithm
s→
Dec
isio
n m
akin
g (“
data
redu
ctio
n”)
→ D
ata
asso
ciat
ion
(spa
ce, t
ime,
freq
uenc
y)
•Net
wor
k-pr
oces
sing
→ D
ata
fusi
on→
Dat
a tra
nsm
issi
on(w
ho?
whe
n? w
hat?
)→
Dat
a as
soci
atio
n (s
pace
, tim
e, fr
eque
ncy)
Cla
udio
da
Silv
a
Dis
tribu
ted
Sen
sing
Alg
orith
m E
xam
ple
P(H
0) =
0.4
1 se
nsor
→
thr=
0.6
72
sens
ors →
thr=
1.2
5
Fusi
on ru
le: O
R
P(H
0) =
0.6
1 se
nsor
→
thr=
1.5
2 se
nsor
s →
thr=
0.8
Fusi
on ru
le: A
ND
Exa
mpl
e: T
wo
inde
pend
ent s
enso
rs, H
0N
(0,1
), an
d H
1N
(1,1
).
Fusi
on c
ente
r:
Rad
ios:
Cla
udio
da
Silv
a
Dis
tribu
ted
Sen
sing
: Exa
mpl
es
Dis
trib
uted
det
ectio
n of
a k
now
n si
gnal
in
AW
GN
, with
opt
imal
th
resh
olds
obt
aine
d by
a G
auss
-Sei
del
itera
tive
algo
rithm
.
Dis
trib
uted
mod
ulat
ion
clas
sific
atio
n (A
WG
N),
usin
g a
cycl
ic fe
atur
e-ba
sed
clas
sifie
r and
op
timal
thre
shol
ds
obta
ined
by
a G
auss
-Se
idel
iter
ativ
e al
gorit
hm.
Prob
lem
: The
oret
ical
bou
nds
on th
e di
strib
uted
spe
ctru
m s
ensi
ng
perfo
rman
ce
Cla
udio
da
Silv
a
Dis
tribu
ted
Sen
sing
: Com
plex
ity•P
robl
em: “
On
the
com
plex
ity o
f dec
entra
lized
dec
isio
n m
akin
g an
d de
tect
ion
prob
lem
s, ..
. our
resu
lts p
oint
to th
e in
here
nt d
iffic
ulty
of d
ecen
traliz
ed d
ecis
ion
mak
ing
and
sugg
est t
hat o
ptim
ality
may
be
an e
lusi
ve g
oal”
[Tsi
tsik
lis’8
5]
•Pos
sibl
e ap
proa
ches
:•I
tera
tive,
mes
sage
-pas
sing
alg
orith
ms
•Gra
phic
al m
etho
ds•D
istri
bute
d de
tect
ion
prob
lem
s ar
e ve
ry w
ell r
epre
sent
ed b
y gr
aphi
cal
mod
els.
Suc
h m
odel
s, in
clud
ing
Bay
esia
n ne
twor
ks, c
aptu
re th
e di
vers
e in
terd
epen
denc
ies
amon
g a
set o
f var
iabl
es (n
ode
obse
rvat
ions
and
dec
isio
ns) a
nd th
e no
des’
con
nect
ions
•The
inte
grat
ion
of th
is s
et o
f var
iabl
es is
then
cap
ture
d in
a
prob
abili
stic
infe
renc
e pr
oble
m (e
.g.,
belie
f pro
paga
tion)
•Oth
er im
porta
nt is
sues
: net
wor
k co
nfig
urat
ion,
pow
er/b
andw
idth
re
quire
men
ts, d
ata
asso
ciat
ion…
Prob
lem
: Des
ign
of e
ffici
ent,
low
-com
plex
ityal
gorit
hms
for d
istri
bute
d sp
ectru
m s
ensi
ng
Cla
udio
da
Silv
a
Spa
tial A
spec
ts o
f Dis
tribu
ted
Sen
sing
Mea
sure
of r
elia
bilit
yC
orre
latio
n
Pos
ition
ing
(you
and
you
r pee
rs):
Ass
ume
that
exp
erim
enta
l res
earc
h sa
ys th
at o
n av
erag
e th
e ob
serv
atio
ns o
f ra
dios
sep
arat
ed b
y on
e ya
rd h
ave
corr
elat
ion
coef
ficie
nt 0
.6. H
ow c
an tw
o co
gniti
ve ra
dios
that
are
sep
arat
ed b
y th
is d
ista
nce
(and
kno
w o
f thi
s fa
ct) t
ake
adva
ntag
e of
this
ave
rage
cor
rela
tion
whe
n pe
rform
ing
sens
ing?
Exa
mpl
e:C
orre
latio
n co
effic
ient
of S
NR
est
imat
es
0.35
0.41
0.47
0.49
ρ10
050
101
d
(R =
100
0, S
NR
t=
70 d
B)
Cla
udio
da
Silv
a
Spa
tial S
pect
rum
: Clu
ster
ing
Prob
lem
: How
sho
uld
a ne
twor
k of
cog
nitiv
e ra
dios
con
figur
e its
elf i
n or
der t
o pe
rform
spe
ctru
m s
ensi
ng?
→ D
o w
e re
ally
wan
t a g
loba
l dec
isio
n?
→ P
ut th
ings
in p
ersp
ectiv
e: n
o ne
ed fo
r CR
sin
Sea
ttle
to k
now
wha
t’s h
appe
ning
in
Bla
cksb
urg
→ G
ranu
larit
y vs
. pow
er/b
andw
idth
sav
ings
(w
arni
ng: m
inim
um n
umbe
r of r
adio
s is
re
quire
d to
ach
ieve
a g
iven
relia
bilit
y)
This
wor
k w
as s
uppo
rted
in p
art b
y a
gift
from
Tex
as In
stru
men
ts.
10-4
10-3
10-2
10-1
100
10-4
10-3
10-2
10-1
100
Pro
b. In
terfe
renc
e (G
rey
Are
a)
Prob. False Spectrum Access Denial
All
10 r
adio
s
2 cl
uste
rs4
clus
ters
Non
-col
labo
rativ
e
10-4
10-3
10-2
10-1
100
10-3
10-2
10-1
100
Pro
b. F
alse
Ala
rm
Prob. Detection
All
10 r
adio
s
2 cl
uste
rs4
clus
ters
Non
-col
labo
rativ
e
Cla
udio
da
Silv
a
The
10 1
00 1
000
1P
roje
ct
Que
stio
n: W
hat i
s th
e be
st re
liabi
lity
we
can
achi
eve
in
su
ch a
sce
nario
?G
oal:
Prob
abilit
y of
inte
rfere
nce
to p
rimar
y sy
stem
equ
al to
0.0
1%an
d pr
obab
ility
of “f
alse
den
ial”
equa
l to
1% fo
r99.
9%of
the
time.
A C
R a
d-ho
c ne
twor
k……
of 1
0co
gniti
ve ra
dios
that
…
by tr
ansm
ittin
g a
tota
l of 1
00bi
ts
each
…by
per
form
ing
1000
DSP
oper
atio
ns…
and
in u
nder
1se
cond
colla
bora
tivel
y pe
rform
spe
ctru
m s
ensi
ng
Cla
udio
da
Silv
a
VT
CR
Net
wor
k Te
stbe
d (V
T-C
OR
NE
T)
Prob
lem
: How
can
we
incr
ease
con
fiden
ce in
the
tech
nolo
gy –
deve
lope
rs,
regu
lato
rs, u
sers
?
•Foc
us a
nd c
ontr
ibut
ion:
PH
Y, c
ogni
tive
engi
ne, a
nd c
ogni
tion
at n
etw
ork
leve
l•I
nstit
ute
for C
ritic
al T
echn
olog
y an
d A
pplie
d Sc
ienc
e (IC
TAS)
Arc
hite
ctur
e•N
ew IC
TAS
bui
ldin
g, c
ontro
lled
rem
otel
y, c
eilin
g•4
8 no
des
to b
e in
stal
led:
12/
floor
x 4
floo
rs•U
SR
P +
RF
Fron
tend
: New
Mot
orol
a ch
ip (1
00M
Hz
to 4
GH
z)•S
oftw
are:
Any
plat
form
. Fi
rst s
tage
: OS
SIE
, an
impl
emen
tatio
n of
the
Join
t Tac
tical
Rad
io S
yste
m’s
S
oftw
are
Com
mun
icat
ion
Arc
hite
ctur
e (S
CA
)
Som
e as
pect
s of
CR
N th
at n
eed
expe
rimen
tal v
erifi
catio
n &
test
ing
•Mod
el a
ccur
acy:
Alg
orith
ms,
pro
toco
ls, a
pplic
atio
ns•C
olle
ct p
erfo
rman
ce a
nd Q
oSm
easu
rem
ents
for f
urth
er a
naly
sis
•Rea
listic
con
ditio
ns•V
erify
legi
timat
e op
erat
ion
of c
ogni
tive
engi
nes
Cla
udio
da
Silv
a
Con
clus
ions
We
have
a lo
t of w
ork
to d
o:•T
heor
etic
al b
ound
s on
the
spec
trum
sen
sing
per
form
ance
•Per
form
ance
lim
its o
f sen
sing
alg
orith
ms
for r
ealis
tic im
plem
enta
tions
an
d pr
actic
al s
cena
rios
•The
oret
ical
bou
nds
on th
e di
strib
uted
spe
ctru
m s
ensi
ng p
erfo
rman
ce•D
esig
n of
effi
cien
t, lo
w-c
ompl
exity
alg
orith
ms
for d
istri
bute
d sp
ectru
m
sens
ing
•How
can
(par
tial a
nd c
ompl
ete)
pos
ition
info
rmat
ion
be u
sed
to im
prov
e sp
ectru
m s
ensi
ng?
•How
sho
uld
a ne
twor
k of
cog
nitiv
e ra
dios
con
figur
e its
elf i
n or
der t
o pe
rform
spe
ctru
m s
ensi
ng?
•How
can
we
incr
ease
con
fiden
ce in
the
tech
nolo
gy –
deve
lope
rs,
regu
lato
rs, u
sers
?